Statistically correlating NMR spectra and LC-MS data to facilitate the identification of individual metabolites in metabolomics mixtures
文献类型：期刊论文
| 作者 | Li, Xing1,2 ; Luo, Huan1; Huang, Tao1,2; Xu, Li1,2; Shi, Xiaohuo1; Hu, Kaifeng1,3
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| 刊名 | ANALYTICAL AND BIOANALYTICAL CHEMISTRY
 |
| 出版日期 | 2019-03-01 |
| 卷号 | 411期号:7页码:1301-1309 |
| 关键词 | Deconvolution LC-MS NMR Statistical correlation Structure identification |
| ISSN号 | 1618-2642 |
| DOI | 10.1007/s00216-019-01600-z |
| 通讯作者 | Hu, Kaifeng(kaifenghu@mail.kib.ac.cn) |
| 英文摘要 | NMR and LC-MS are two powerful techniques for metabolomics studies. In NMR spectra and LC-MS data collected on a series of metabolite mixtures, signals of the same individual metabolite are quantitatively correlated, based on the fact that NMR and LC-MS signals are derived from the same metabolite covary. Deconvoluting NMR spectra and LC-MS data of the mixtures through this kind of statistical correlation, NMR and LC-MS spectra of individual metabolites can be obtained as if the specific metabolite is virtually isolated from the mixture. Integrating NMR and LC-MS spectra, more abundant and orthogonal information on the same compound can significantly facilitate the identification of individual metabolites in the mixture. This strategy was demonstrated by deconvoluting 1D C-13, DEPT, HSQC, TOCSY, and LC-MS spectra acquired on 10 mixtures consisting of 6 typical metabolites with varying concentration. Based on statistical correlation analysis, NMR and LC-MS signals of individual metabolites in the mixtures can be extracted as if their spectra are acquired on the purified metabolite, which notably facilitates structure identification. Statistically correlating NMR spectra and LC-MS data (CoNaM) may represent a novel approach to identification of individual compounds in a mixture. The success of this strategy on the synthetic metabolite mixtures encourages application of the proposed strategy of CoNaM to biological samples (such as serum and cell extracts) in metabolomics studies to facilitate identification of potential biomarkers. |
| WOS研究方向 | Biochemistry & Molecular Biology ; Chemistry |
| 语种 | 英语 |
| WOS记录号 | WOS:000463593300001 |
| 源URL | [http://ir.kib.ac.cn/handle/151853/67624]  |
| 专题 | 昆明植物研究所_植物化学与西部植物资源持续利用国家重点实验室 |
| 通讯作者 | Hu, Kaifeng |
| 作者单位 | 1.Chinese Acad Sci, Kunming Inst Bot, State Key Lab Phytochem & Plant Resources West Ch, 132 Lanhei Rd, Kunming 650201, Yunnan, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chengdu Univ TCM, Innovat Inst Chinese Med & Pharm, Chengdu 611137, Sichuan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Xing,Luo, Huan,Huang, Tao,et al. Statistically correlating NMR spectra and LC-MS data to facilitate the identification of individual metabolites in metabolomics mixtures[J]. ANALYTICAL AND BIOANALYTICAL CHEMISTRY,2019,411(7):1301-1309. |
| APA | Li, Xing,Luo, Huan,Huang, Tao,Xu, Li,Shi, Xiaohuo,&Hu, Kaifeng.(2019).Statistically correlating NMR spectra and LC-MS data to facilitate the identification of individual metabolites in metabolomics mixtures.ANALYTICAL AND BIOANALYTICAL CHEMISTRY,411(7),1301-1309. |
| MLA | Li, Xing,et al."Statistically correlating NMR spectra and LC-MS data to facilitate the identification of individual metabolites in metabolomics mixtures".ANALYTICAL AND BIOANALYTICAL CHEMISTRY 411.7(2019):1301-1309. |
入库方式： OAI收割
来源：昆明植物研究所
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